POS0529 USING SOCIAL MEDIA CONVERSATIONS TO UNDERSTAND PATIENT CARE: FACTORS DRIVING PROACTIVE VS REACTIVE MANAGEMENT OF GOUT

Background

To understand the needs of a particular community, it is imperative to actively listen to and interpret the patient experience. We used a proprietary artificial intelligence (AI) analytics engine that uses natural language processing to evaluate social media conversations in online gout communities. Gout is a chronic disease defined by uric acid crystal deposits which induce painful arthritis flares/flare-ups ^[1]. Managing gout can be characterized by two approaches: proactive and reactive management. Proactive management refers to scheduled, prophylactic care (e.g., regular doctor visits, treating underlying illness), whereas reactive management is spontaneous care driven by symptom onset (e.g., urgent care/walk-in clinic visits). The ideal management strategy is debated. Subspecialty groups recommend a proactive “treat-to-target” strategy focused on uric acid. The American College of Physicians recommends “treat-to-symptom control” without a “treat-to-uric acid-target” strategy. We assessed patient views on each to improve our understanding of these management methods.

Objectives

The current study aimed to identify gout symptoms associated with reactive management. We also wanted to contrast the sentiment of online gout community conversations when describing proactive vs reactive therapeutic experiences.

Methods

We evaluated 2 social media sources: a private Facebook group, The Gout Support Group of America (1000+ members, 99 countries), which had 50,000 posts/comments gathered in 2021-2022; and a public subreddit (r/gout) (9000+ members) with 125,000 posts/comments from 2011-2022. Our AI engine first tagged all posts/comments discussing proactive or reactive care experiences. Entity recognition was then used to identify the most frequently mentioned clinical findings in conversations by care type. We then fit a logistic regression model in which clinical finding mentions predicted care type. To characterize the general sentiment of conversations, the engine scored all posts/comments from −1 (most negative) to 1 (most positive) using a pretrained sentiment tagger.

Results

Flares, pain, uric acid, and swelling were the most frequently mentioned in both proactive and reactive conversations. Reactive care gout conversations (n = 1253 posts/comments from 624 users) were associated with a significantly higher probability of mentioning ‘pain’ and ‘swelling’ and a significantly lower probability of mentioning ‘uric acid’ than were proactive care conversations (n = 1205 posts/comments, 521 users). Mentioning ‘flares’ did not significantly impact the probability of mentioning either care type. Sentiment analysis showed that reactive care statements had a significantly lower mean sentiment score; indicating discussions about reactive care experiences tended to be more negative than those about proactive care.

Conclusion

In analyzing gout social media posts, we found that flares, pain, swelling, and concerns related to uric acid were primary motivators for individuals seeking gout care. Conversations mentioning ‘pain’ were twice as likely to mention reactive care compared to proactive gout conversations. Analysis also showed that reactive care gout conversations tended to be more negative, supporting the position that proactive management may be more beneficial for individuals with gout overall. This type of information can be used to identify and address patients’ areas of concern or dissatisfaction. Future work should continue exploring these patient-reported perspectives and experiences so clinicians, caregivers, and patients can better understand and guide care-based management decisions.

References

[1]Mikuls TR. Gout. N Engl J Med. 2022;387(20):1877-1887. doi:10.1056/NEJMcp2203385

Acknowledgements

The authors would like to thank our TREND Community managers Matthew Horsnell and Rachelle Cook for their contribution in providing advocacy and support for the gout community; and the private Facebook group, Gout Support Group of America, for providing access to data during the preparation of this abstract. Funding for this work was provided by Horizon Therapeutics.

Disclosure of Interests

Maurice Flurie Grant/research support from: Our clients are pharmaceutical and biotechnology companies including, but not limited to Horizon Therapeutics, Chiesi Global Rare Disease, Novartis, Harmony Biosciences, and Avadel. TREND Community: employee, Monica Converse Grant/research support from: Our clients are pharmaceutical and biotechnology companies including, but not limited to Horizon Therapeutics, Chiesi Global Rare Disease, Novartis, Harmony Biosciences, and Avadel. TREND Community: employee, Kristina Davidson Shareholder of: Horizon Therapeutics, Employee of: Horizon Therapeutics, Daniel Hernandez: None declared, Helen Hernandez: None declared, Gary Ho Grant/research support from: Horizon Therapeutics, Brian LaMoreaux Shareholder of: Horizon Therapeutics, Employee of: Horizon Therapeutics, Christopher Parker Speakers bureau: Horizon Therapeutics, Christopher DeFelice Grant/research support from: Our clients are pharmaceutical and biotechnology companies including, but not limited to Horizon Therapeutics, Chiesi Global Rare Disease, Novartis, Harmony Biosciences, and Avadel. TREND Community: owner, Maria Picone Grant/research support from: Our clients are pharmaceutical and biotechnology companies including, but not limited to Horizon Therapeutics, Chiesi Global Rare Disease, Novartis, Harmony Biosciences, and Avadel. TREND Community: owner, E. Robert Wassman Grant/research support from: Our clients are pharmaceutical and biotechnology companies including, but not limited to Horizon Therapeutics, Chiesi Global Rare Disease, Novartis, Harmony Biosciences, and Avadel. TREND Community: employee.